Dirichlet Process prior: The default distribution of latent features on each representation space is approximated via Gaussian Mixture Model.
However, Gaussian Mixture Model has several hyperparameters, , number of components, that need manual selection.
Consider the infinite number of components,
an alternative way is to apply Dirichlet Process prior as a distribution of mixture weights over Gaussian distribution components [1]}.
Naturally, many of these components will be redundant, and their mixing weights are close to 0, so that we could ignore them from probability mixture.
In the implementation, the Dirichlet Process inference uses a truncated distribution with a fixed maximum number of components.
Therefore, the actual number of components used always depends on the data.
